We are discussing changepoint detection in tropospheric parameter time series that occurs in a numerical weather reanalysis model. Our approach applies a statistical method that is based on the maximum value of two sample t-statistics. We use critical values calculated by applying an asymptotic distribution. We also apply an asymptotic distribution to finding approximate critical values for the changepoint position. Experiments on “test” and “real” data illustrate the assumed accuracy and efficiency of our method. The method is assessed by its application to our series after adding synthetic shifts. A total of more than 3,000 original profiles are then analysed within the time-span of the years 1990-2015. The analysis shows that at least one changepoint is present in more than 9% of the studied original time series. The uncertainty of estimated times achieved tens of days for shifts larger than 9 mm, but it was increased up to hundreds of days in the case of smaller synthetic shifts. Discussed statistical method has potential for suspected change point detection in time series with higher time resolution.
We build a multi-stage stochastic program of an asset-liability management problem of a leasing company, analyse model results and present a stress-testing methodology suited for financial applications. At the beginning, the business model of such a company is formulated. We introduce three various risk constraints, namely the chance constraint, the Value-at-Risk constraint and the conditional Value-at-Risk constraint along with the second-order stochastic dominance constraint, which are applied to the model to control risk of the optimal strategy. We also present the structure and the generation process of our scenarios. To capture the evolution of interest rates the Hull-White model is used. Thereafter, results of the model and the effect of the risk constraints on the optimal decisions are thoroughly investigated. In the final part, the performance of the optimal solutions of the problems for unconsidered and unfavourable crisis scenarios is inspected. The methodology of a stress test we used was proposed in such a way that it answers typical questions asked by asset-liability managers.
The neutral differential equation (1.1) $$ \frac{{\mathrm{d}}^n}{{\mathrm{d}} t^n} [x(t)+x(t-\tau)] + \sigma F(t,x(g(t))) = 0, $$ is considered under the following conditions: $n\ge 2$, $\tau >0$, $\sigma = \pm 1$, $F(t,u)$ is nonnegative on $[t_0, \infty) \times (0,\infty)$ and is nondecreasing in $u\in (0,\infty)$, and $\lim g(t) = \infty$ as $t\rightarrow \infty$. It is shown that equation (1.1) has a solution $x(t)$ such that (1.2) $$ \lim_{t\rightarrow \infty} \frac{x(t)}{t^k}\ \text{exists and is a positive finite value if and only if} \int^{\infty}_{t_0} t^{n-k-1} F(t,c[g(t)]^k){\mathrm{d}} t < \infty\text{ for some }c > 0. $$ Here, $k$ is an integer with $0\le k \le n-1$. To prove the existence of a solution $x(t)$ satisfying (1.2), the Schauder-Tychonoff fixed point theorem is used.
Does there exist an atomic lattice effect algebra with non-atomic subalgebra of sharp elements? An affirmative answer to this question (and slightly more) is given: An example of an atomic MV-effect algebra with a non-atomic Boolean subalgebra of sharp or central elements is presented.
On the example of Ostrów Tumski in Wrocław (Silesia, Polish lands) an attempt of identification of places associated with trade and exchange in early medieval strongholds was made. The alleged marketplace existed in the stronghold from the end of the 11th century to the end of the 12th century. Its existence would be confirmed by the empty square between buildings, the accumulation of finds related to trade and exchange as well as spending free time. The connection of some buildings form Ostrów Tumski with commercial activities is also not excluded. On the basis of analogies from the main centers of the Piast state and selected sites from the Bohemian and Moravian territories, the potential location of the marketplaces was analyzed. Attention was drawn to the relationship between finds related to trade and exchange with other manifestations of economic activity (e.g. non-ferrous metallurgy). An attempt was also made to answer the question when and in what political and economic conditions such places could be created.
An effective and novel roller bearing fault diagnosis technique based on empirical mode decomposition (EMD) energy entropy and support vector machine (SVM) is put forward in this article. The vibration signal of roller bearing is decomposed by EMD and the first 5 intrinsic mode function (IMF) components are obtained. SVM served as a fault diagnosis classifier and the extracted energy features of the first 5 IMFs are taken as network input vectors, and then the fault bearing and the normal bearing can be distinguished. An technique for fault of roller bearing by SVM is evaluated against a series of fault diagnosis methods that are widely used in machinery, with particular regard to the effect of training set size on fault diagnosis accuracy. We trained the SVM using RBF kernel function. We compare our experimental results with the existing results given by SMO and SVM-light algorithms. It can be seen that the fault diagnosis method based on SVM-light is superior to that based on SMO in diagnosis accuracy of roller bearing. In addition to the SVM, the same datasets were classified using RBF NN and Hopfield NN. The experimental results show that the technique of support vector machine based on EMD energy entropy has higher fault diagnosis ability.
Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for the translation vectors at this moment. In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm - specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm - in initializing the translation vectors of the WNNs. The effectiveness of embedding different activation functions in WNNs will be investigated as well. The categorization effectiveness of the proposed WNNs model was then evaluated in classifying the type 2 diabetics, and was compared with the multilayer perceptrons (MLPs) and radial basis function neural networks (RBFNNs) models. Performance assessment shows that our proposed model outperforms the rest, since a 100% superior classification rate was achieved.
Color quantization is an important process for image processing and various applications. Up to now, many color quantization methods have been proposed. The self-organizing maps (SOM) method is one of the most effective color quantization methods, which gives excellent color quantization results. However, it is slow, so it is not suitable for real-time applications. In this paper, we present a color importance{based SOM color quantization method. The proposed method dynamically adjusts the learning rate and the radius of the neighborhood using color importance. This makes the proposed method faster than the conventional SOM-based color quantization method. We compare the proposed method to 10 well-known color quantization methods to evaluate performance. The methods are compared by measuring mean absolute error (MAE), mean square error (MSE), and processing time. The experimental results show that the proposed method is effective and excellent for color quantization. Not only does the proposed method provide the best results compared to the other methods, but it uses only 67.18% of the processing time of the conventional SOM method.
An algorithm for correction of two-axes laser mount direction and gates in case of atmospheric drag induces diviations from the predicted satellite motion is described. Results of numerical simulations are shown.